AI Models & Companies · AI Model Releases and Versioning

How quickly do older AI model versions get deprecated?

AI model deprecation timelines vary by provider and by whether it's a consumer-facing or API-accessed model, but major providers commonly give developers a deprecation notice period ranging from a few months to roughly a year before an older API model version stops working, while consumer chat apps often phase out older models with less advance notice.

Key takeaways

API-accessed models typically come with a published deprecation timeline once a newer replacement is available.
Deprecation notice periods commonly range from a few months to around a year, varying by provider.
Consumer-facing chat apps often transition users to newer models with less formal advance notice than API deprecations.
Building software that depends on a specific model version requires actively monitoring provider deprecation announcements.

Deprecation Timelines Vary by Provider

There’s no universal standard for how quickly an AI provider deprecates an older model version — timelines vary meaningfully between OpenAI, Anthropic, Google, and others, and each provider maintains its own policy, typically documented in its developer or API documentation.

API Models Usually Come With Formal Notice

For models accessed through a developer API, major providers commonly give a published deprecation notice period — often ranging from a few months to roughly a year — once a newer replacement model becomes available, giving developers time to test and migrate their applications before the older version stops functioning.

Consumer Apps Often Move Faster and Less Formally

Consumer-facing chat products (like a chat app’s default model) often transition users to newer models with less formal advance notice than API deprecations receive, since the provider directly controls the interface and can shift the default model without requiring end users to take any action.

What This Means for Anyone Building on a Specific Model

Any application or workflow built to depend on a specific model version needs an active process for monitoring that provider’s deprecation announcements — treating a specific model version as permanently stable, without a plan for eventual migration, creates real risk of unexpected breakage once that version is retired.

A Practical Recommendation

For any production application, building in periodic checks of provider deprecation announcements — rather than discovering a breaking change only after it happens — is standard practice among experienced developers working with AI APIs. Many providers also offer a way to request notification of upcoming deprecations directly, which is worth setting up proactively rather than relying on catching an announcement after the fact.

See the Full AI Model Release Timeline

Track every major model release from OpenAI, Anthropic, and Google since GPT-4 with our free AI Model Release Timeline — filterable by provider.

Go deeper

Should a business build its own custom ai model or use an existing provider api?

Frequently asked questions

What happens if I'm using an AI model through an API and it gets deprecated without me updating my code?

Typically the API call will start failing or automatically route to a replacement model once the deprecation date passes, depending on the specific provider's policy — which is why actively monitoring a provider's deprecation announcements matters for any application built on a specific model version.

Do all AI providers publish their deprecation schedules the same way?

No — providers vary in how far in advance they announce deprecations and how they communicate them (developer changelogs, email notifications, documentation updates), so checking each specific provider's own developer documentation is the most reliable way to stay current.

Sources

[1]Pricing | OpenAI API — OpenAI

Written by Editorial Team

Last updated August 12, 2026

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